From 588d796014d70db184bb2bb6e99a11e9687820b4 Mon Sep 17 00:00:00 2001
From: LFX <736511897@qq.com>
Date: Thu, 7 Nov 2024 18:55:40 +0800
Subject: [PATCH] Create the data folder for the healthcare model zoo
---
examples/healthcare/data/malaria.py | 122 ++++++++++++++++++++++++++++
1 file changed, 122 insertions(+)
create mode 100644 examples/healthcare/data/malaria.py
diff --git a/examples/healthcare/data/malaria.py b/examples/healthcare/data/malaria.py
new file mode 100644
index 000000000..46422b739
--- /dev/null
+++ b/examples/healthcare/data/malaria.py
@@ -0,0 +1,122 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+try:
+ import pickle
+except ImportError:
+ import cPickle as pickle
+
+import numpy as np
+import os
+import sys
+from PIL import Image
+
+
+# need to save to specific local directories
+def load_train_data(dir_path="/tmp/malaria", resize_size=(128, 128)):
+ dir_path = check_dataset_exist(dirpath=dir_path)
+ path_train_label_1 = os.path.join(dir_path, "training_set/Parasitized")
+ path_train_label_0 = os.path.join(dir_path, "training_set/Uninfected")
+ train_label_1 = load_image_path(os.listdir(path_train_label_1))
+ train_label_0 = load_image_path(os.listdir(path_train_label_0))
+ labels = []
+ Images = np.empty((len(train_label_1) + len(train_label_0),
+ 3, resize_size[0], resize_size[1]), dtype=np.uint8)
+ for i in range(len(train_label_0)):
+ image_path = os.path.join(path_train_label_0, train_label_0[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i] = temp_image
+ labels.append(0)
+ for i in range(len(train_label_1)):
+ image_path = os.path.join(path_train_label_1, train_label_1[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i + len(train_label_0)] = temp_image
+ labels.append(1)
+
+ Images = np.array(Images, dtype=np.float32)
+ labels = np.array(labels, dtype=np.int32)
+ return Images, labels
+
+
+# need to save to specific local directories
+def load_test_data(dir_path='/tmp/malaria', resize_size=(128, 128)):
+ dir_path = check_dataset_exist(dirpath=dir_path)
+ path_test_label_1 = os.path.join(dir_path, "testing_set/Parasitized")
+ path_test_label_0 = os.path.join(dir_path, "testing_set/Uninfected")
+ test_label_1 = load_image_path(os.listdir(path_test_label_1))
+ test_label_0 = load_image_path(os.listdir(path_test_label_0))
+ labels = []
+ Images = np.empty((len(test_label_1) + len(test_label_0),
+ 3, resize_size[0], resize_size[1]), dtype=np.uint8)
+ for i in range(len(test_label_0)):
+ image_path = os.path.join(path_test_label_0, test_label_0[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i] = temp_image
+ labels.append(0)
+ for i in range(len(test_label_1)):
+ image_path = os.path.join(path_test_label_1, test_label_1[i])
+ temp_image = np.array(Image.open(image_path).resize(
+ resize_size).convert("RGB")).transpose(2, 0, 1)
+ Images[i + len(test_label_0)] = temp_image
+ labels.append(1)
+
+ Images = np.array(Images, dtype=np.float32)
+ labels = np.array(labels, dtype=np.int32)
+ return Images, labels
+
+
+def load_image_path(list):
+ new_list = []
+ for image_path in list:
+ if (image_path.endswith(".png") or image_path.endswith(".jpg")):
+ new_list.append(image_path)
+ return new_list
+
+
+def check_dataset_exist(dirpath):
+ if not os.path.exists(dirpath):
+ print(
+ 'Please download the malaria dataset first'
+ )
+ sys.exit(0)
+ return dirpath
+
+
+def normalize(train_x, val_x):
+ mean = [0.5339, 0.4180, 0.4460] # mean for malaria dataset
+ std = [0.3329, 0.2637, 0.2761] # std for malaria dataset
+ train_x /= 255
+ val_x /= 255
+ for ch in range(0, 2):
+ train_x[:, ch, :, :] -= mean[ch]
+ train_x[:, ch, :, :] /= std[ch]
+ val_x[:, ch, :, :] -= mean[ch]
+ val_x[:, ch, :, :] /= std[ch]
+ return train_x, val_x
+
+
+def load(dir_path):
+ train_x, train_y = load_train_data(dir_path=dir_path)
+ val_x, val_y = load_test_data(dir_path=dir_path)
+ train_x, val_x = normalize(train_x, val_x)
+ train_y = train_y.flatten()
+ val_y = val_y.flatten()
+ return train_x, train_y, val_x, val_y